UTILIZATION OF ARTIFICIAL INTELLIGENCE FOR PREDICTIVE MODELING IN DENTAL IMPLANTOLOGY.

Artificial intelligence (AI) is making waves in dentistry, with applications in predicting dental implant success. AI models analyze patient data (X-rays, medical history) to identify factors influencing implant viability. The aim is to identify existing research on the use of AI-based predictive models in dental implants. The following databases were searched: Web of Science, Scopus, Google Scholar, PubMed, and Cochrane Library, using the keywords "Artificial Intelligence," "Dentistry," "Implant," and "Success." The studies were reviewed qualitatively, as quantitative analysis was not feasible due to the lack of specific outcomes and the insufficient number of studies for comparison. Technology has presented dental implantology with many opportunities, and it is through artificial intelligence that it is advancing. AI is being applied to detect potential implant failure patients, prognosis osseointegration, improve implant design, and master planning and also for data analysis to predict early complications. Decision trees, random forests, Artificial Neural Networks (ANN), and Deep Learning (DL) improve diagnostics and treatment planning and introduce a powerful predictive model for a successful implant.

A forecasting model for suitable dental implantation in canine mandibular premolar region based on finite element analysis.

In recent years, dental implants have become a trend in the treatment of human patients with missing teeth, which may also be an acceptable method for companion animal dentistry. However, there is a gap challenge in determining appropriate implant sizes for different dog breeds and human. In this study, we utilized skull computed tomography data to create three-dimensional models of the mandibles of dogs in different sizes. Subsequently, implants of various sizes were designed and subjected to biomechanical finite element analysis to determine the optimal implant size. Regression models were developed, exploring the relationship between the average weight of dogs and the size of premolar implants. Our results illustrated that the regression equations for mean body weight (x, kg) and second premolar (PM2), third premolar (PM3), and fourth premolar (PM4) implant length (y, mm) in dogs were: y = 0.2785x + 7.8209, y = 0.2544x + 8.9285, and y = 0.2668x + 10.652, respectively; the premolar implant diameter (mm) y = 0.0454x + 3.3506, which may provide a reference for determine suitable clinical implant sizes for dogs.

From Conventional to Complex Treatments, Digital Technologies Are Simplifying Implant Dentistry.

In the past decade, the shifting of dentistry into the digital world has become obvious to any dentist or dental specialist. Many restorative dentists now consider themselves "digital dentists," performing all their crown and bridge restorative planning and fabrication in a digital environment. In preparation for this article, the author asked both a prosthodontist and the owner of a large state-of-the-art dental laboratory with whom he has worked closely the following questions: "These days, what percentage of your crown and bridge cases are being done fully digital?" Then, "What is the answer to the same question 5 years ago?" The prosthodontist answered that for crowns and bridges (with some exceptions for bridges), including implants (but not full arches), "close to 100 percent" of cases are now fully digital, while "maybe 25 percent" were 5 years ago (personal communication with Jason Sauer, DDS, of Westchester Prosthodontics, White Plains, New York, March 29, 2024). The dental laboratory owner answered, "My lab is different than others. We're about 60 percent digital, but digital is rapidly increasing. I'd say the majority of labs are 80 to 90 percent digital. Five years ago, we were 70 to 80 percent hand (analog.) We went from three mills pre-Covid to 30 different machines post-Covid (printers, mills, and scanners)" (personal communication with Steven Pigliacelli, MDT, of Marotta Dental Studio, Farmingdale, New York, March 29, 2024). It's fair to say that these comments show that the implementation of digital technologies in dentistry has been dramatic and will continue to expand.

Effects of flapless and flapped implantations on soft tissue: a systematic review and Meta-analysis.

OBJECTIVES: This study aimed to systematically compare the effects of flapless and flapped implantations on the surrounding soft tissues of dental implants. METHODS: Nine databases were searched, including PubMed, Embase, Cochrane Library, Web of Science, Clinical Trials, OpenGrey, OpenDoar, Scopus, and Ovid, from January 1, 2013, to August 27, 2023. Randomized controlled trials comparing flapless implantation with flapped implantation for restoration of missing teeth were included. Meta-analysis was conducted on studies that met the inclusion criteria by using RevMan 5.3 and Stata 14.0. RESULTS: A total of 1 245 articles were retrieved, and 17 studies were ultimately included. The results of Meta-analysis showed that flapless implantation resulted in better healing of the soft tissues around the dental implants than flapped implantation. Moreover, flapless implantation showed superior changes in implant success rate [mean difference (MD)=1.06, 95% confidence interval (CI) (1.02, 1.10), P=0.004], the width of keratinized gingival changes [MD=0.10, 95%CI (0.00, 0.20), P=0.04], and probing depth [MD=-0.60, 95%CI (-0.67, -0.53), P<0.000 01], with statistically significant differences. The final combined results of modified plaque index [standardized mean difference (SMD)=-0.41, 95%CI (-0.81, 0.00), P=0.05] and modified sulcus bleeding index [SMD=-0.44, 95%CI (-0.78, -0.10), P=0.01] showed superiority over flapped implantation. The papillary presence index was higher in the flapless implantation group than in the flapped implantation group. No statistically significant differences were observed in plaque index and gingival indices changes between the two groups. CONCLUSIONS: Flapless implantation can achieve higher implant success rate, smaller changes in the width of keratinized gingival, and smaller probing depths than flapped implantation. It also has advantages in terms of modified plaque index, modified sulcus bleeding index, and papillary presence index.

[Determination of the presence of an antibiotic in implant-bone biopsy specimens by Raman spectroscopy in experiment]. / Opredelenie nalichiya antibiotika v implantato-kostnykh bioptatakh metodom ramanovskoi spektroskopii v eksperimente.

THE AIM OF THE STUDY: Was to determine the presence of an amoxicillin-based antibiotic in bone implant biopsies by Raman spectroscopy in an experiment. MATERIALS AND METHODS: Experimental animals (n=10, a miniature pig of the Svetlogorsk breed) were divided into 2 groups of 5 animals. Groups 1 and 2 were injected with amoxicillin 2 ml per 20 kg of body weight 30 minutes before dental implantation surgery, then group 2 was additionally injected with 1 ml per 20 kg of body weight for 5 days. Each animal has 6 implants installed. On the 1st, 3rd, 7th, 14th day, an implant-bone biopsy was removed from each animal, micro-preparations were made and Raman spectroscopy was performed to assess the peak matching of the Raman spectrum. RESULTS: In animals of the 1st and 2nd groups, the main peak of the Raman spectrum, which is closest to the values of the antibiotic spectrum of interest to us, is located closer to 1448 cm-1 and 1446 cm-1, respectively. At the same time, in both observations, the peaks relate to the spectrum of bone tissue, which cannot indicate the content of an antibiotic in the drug. CONCLUSION: No scattering spectra corresponding to the antibiotic molecule were found in any animal from both groups, regardless of the mode of administration and dosage of amoxicillin. The detected peaks corresponded to bone tissue without an antibiotic.

[Teeth autotransplantation as an alternative to dental implantation]. / Autotransplantatsiya zuba kak al'ternativa dental'noi implantatsii.

The stages of preoperative planning and surgical treatment of compromised teeth by the method of autotransplantation of a tooth are presented on the example of two clinical cases. The autotransplantation method can be considered an alternative to dental implantation when strictly adhering to the surgical protocol and careful selection of patients.

Comparative Evaluation of Normal Saline Gel and Ozone Therapy on Soft and Hard Tissue Health in Dental Implant Surgery.

AIMS: To comparatively evaluate the effect of normal saline gel and ozonated saline-ozonated gel (ozone therapy) on pain, inflammation, soft tissue, and crestal bone loss in dental implant surgery. METHODS AND MATERIAL: Forty adult patients scheduled to undergo implant were randomized into two groups: Twenty patients (n = 20) received ozone therapy and controls (n = 20) received normal saline and gel during implant placement. Inflammation and pain were noted at days 1 and 7 and 3 month intervals by estimating C-reactive protein (CRP) levels and assessing visual analogue scale (VAS) scores. At 3 months, soft tissue outcomes were noted in terms of plaque index, gingival index, and pocket depth, while crestal bone loss was noted via a radiograph. RESULTS: Mean CRP levels were significantly higher in the control group as compared to that in the case group on day 1 and day 7 follow-ups (P < 0.05). Mean VAS scores for pain were also lower in the case group as compared to the control group at all follow-ups, but the difference was significant statistically only at day 1 (P = 0.061). The plaque index was significantly lower in the case group as compared to the control group (P = 0.011) at final follow-up. No significant difference between two groups was observed for crestal bone loss. CONCLUSIONS: Ozone therapy during implant placement was effective in reduction of pain, systemic inflammation, and plaque deposition in dental implant patients.

Deep learning-based approach for 3D bone segmentation and prediction of missing tooth region for dental implant planning.

Recent studies have shown that dental implants have high long-term survival rates, indicating their effectiveness compared to other treatments. However, there is still a concern regarding treatment failure. Deep learning methods, specifically U-Net models, have been effectively applied to analyze medical and dental images. This study aims to utilize U-Net models to segment bone in regions where teeth are missing in cone-beam computerized tomography (CBCT) scans and predict the positions of implants. The proposed models were applied to a CBCT dataset of Taibah University Dental Hospital (TUDH) patients between 2018 and 2023. They were evaluated using different performance metrics and validated by a domain expert. The experimental results demonstrated outstanding performance in terms of dice, precision, and recall for bone segmentation (0.93, 0.94, and 0.93, respectively) with a low volume error (0.01). The proposed models offer promising automated dental implant planning for dental implantologists.

[Application of machine vision image processing technology in dental implant surgery].

Machine vision image processing technology is extensively employed in the medical realm, particularly in dynamic navigation and robotic systems for oral implantology. It plays a pivotal role in assisting clinicians with precise implant placements, enhancing the predictability of implant restorations. The fundamental principles of machine vision image processing technology utilized in dynamic navigation and robotic systems for oral implantology primarily encompass spatial positioning and registration. However, due to variations in technical principles among different systems, their workflows and technical nuances exhibit distinctive characteristics. Therefore, commencing from the principles of spatial positioning and registration in machine vision image processing technology. This article delves into the current application landscape of machine vision in dynamic navigation and robotics for oral implantology. Its objective is to furnish valuable insights for the clinical implementation of machine vision-assisted implant technology.

Retrospective evaluation and descriptive analysis of the prevalence of anatomical structures and variations in CBCT images before dental implant planning in a group Turkish population, part I.

PURPOSE: The aim of this study is to emphasize the importance of using cone-beam computed-tomography in order to determine the anatomical structures and their variations before the treatment in patients who apply to the dentist clinic for implant treatment. METHODS: In the study, CBCT images of 500 adult patients (240 female and 260 male), aged between 21 and 82 years, who applied for implant treatment due to missing teeth, were retrospectively analyzed. Anatomical structures and variations such as nasopalatine canal(NPC), canalis sinuosus(CS), antral alveolar artery(AAA), were evaluated in multiplanar reformation(MPR) sections which are axial, sagittal and coronal can be viewed in consistence with each other. RESULTS: The incidence of anatomical variation of CS in the right region was similar according to gender. The "Funnel" appearance of the NPC shape was found most common shape in both female and male patients (n = 89; 37.1% for females and n = 71; 27.3% for males). CONCLUSION: CBCT has become the most popular 3D imaging method in dental practice especially for planning dental implants. Understanding the relationship with important anatomical structures before implant applications is crucial in dealing with potential complications that may arise afterward. Assessing significant anatomical structures by CBCT and taking precautions against possible risks facilitate the work of dentists during and after procedures.

Applications of Machine Learning in Periodontology and Implantology: A Comprehensive Review.

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Selected osteointegration markers in different timeframes after dental implantation: findings and prognostic value.

The study aimed to determine the osteointegration markers after dental implantation and evaluate their predictive value. The study was performed on 60 practically healthy persons who needed teeth rehabilitation using dental implants. The conical-shaped implants (CI) and hexagonal implants (HI) were used. The content of Osteopontin (OPN), Osteocalcin (OC), Alkaline Phosphatase (ALP), Osteoprotegerin (OPG), and nitric oxide (NO) was determined in patients' gingival crevicular fluid (GCF) and peri-implant sulcular fluid (PISF), collected 1, 3, and 6 months after implantation. During the 3-6 months of observation level of OPN increased in patients with CIs (<50 years > 50 years) and HIs (<50 years) (CI: <50 years F = 36.457, p < 0.001; >50 years F = 30.104, p < 0.001; HI < 50 years F = 2.246, p < 0.001), ALP increased in patients with CIs (<50 years: F = 19.58, p < 0.001; >50 years: F = 12.01; p = 0.001) and HIs (<50 years) (F = 18.51, p < 0.001), OC increased in patients <50 years (CI: F = 33.72, p < 0.001; HI: F = 55.57, p < 0.001), but in patients >50 years - on the 3 days month (CI: F = 18.82, p < 0.001; HI: F = 26.26, p < 0.001), but sharply decreased at the end of sixth month. OPG increased during 1-3 months of the observation in patients <50 years (CI: F = 4.63, p = 0.037; HI: F = 2.8927, p = 0.046), but at the end of the sixth month returned to the initial level; NO content in PISF increased in patients with CI (>50 years) during 1-6 months of the observation (F = 27.657, p < 0.001). During the post-implantation period, age-related differences in osteointegration were observed. Patients <50 years old had relatively high levels of OPN, ALP, OC, and OPG in PISF, resulting in less alveolar bone destruction around dental implants and more intensive osteointegration. These indicators may be used as biological markers for monitoring implant healing. The process of osseointegration was more intense in CIs due to their comparatively high mechanical loading.

Preoperative assessment of bone density for dental implantation: a comparative study of three different ROI methods.

BACKGROUND: Dental cone beam computed tomography (CBCT) is commonly used to evaluate cancellous bone density before dental implant surgery. However, to our knowledge, no measurement approach has been standardized yet. This study aimed to evaluate the relationship between three different regions of interest (ROI) methods on cancellous bone density at the dental implant site using dental CBCT images. METHODS: Patients' dental CBCT images (n = 300) obtained before dental implant surgery were processed using Mimics (Materialise, Leuven, Belgium). At the potential implant sites, the rectangle, cylinder, and surrounding cylinder ROI methods were used to measure bone density. Repeated measures one-way analysis of variance was performed to compare the three ROI methods in terms of measurement results. Pearson correlation analysis was performed to identify the likely pair-wise correlations between the three ROI methods. RESULTS: The density value obtained using the surrounding cylinder approach (grayscale value [GV],523.56 ± 228.03) was significantly higher than the values obtained using the rectangle (GV, 497.04 ± 236.69) and cylinder (GV,493 ± 231.19) ROI methods in terms of results. Furthermore, significant correlations were noted between the ROI methods (r > 0.965; p < 0.001). CONCLUSIONS: The density measured using the surrounding cylinder method was the highest. The choice of method may not influence the trends of measurement results. TRIAL REGISTRATION: This study was approved by the Institutional Review Board of China Medical University Hospital, No. CMUH111-REC3-205. Informed consent was waived by the Institutional Review Board of China Medical University Hospital, CMUH111-REC3-205, owing to the retrospective nature of the study.

The role of the general dental practitioner in the implant journey.

The relationship between referring dentist and implantologist can be crucial in making the treatment journey as efficient and effective as possible for patients. There are a number of considerations to discuss to allow effective communication of options with the patient, as well as the long-term management of these cases once returned to general practice.

Robot-assisted surgery for dental implant placement: A narrative review.

OBJECTIVE: To determine the current status and accuracy of robotic computer-assisted implant surgery (CAIS) applications by examining the associated clinical and experimental outcomes. DATA AND SOURCES: PubMed, Medline, and Cochrane Library databases were searched for relevant studies published between January 2000 and November 2023, and focusing on robotic CAIS in dental implant surgery. All search results were then manually reviewed to identify only the pertinent articles. Only in vitro and clinical studies were included in this narrative review, with implant placement accuracy considered the main outcome. RESULT: Based on our inclusion and exclusion criteria, we included 21 studies (with 1085 implant sites); of them, 8 were clinical studies, 12 were in vitro studies, and 1 included both an in vitro study and a case series. The ranges of the mean implant shoulder, apical, and angular deviations were respectively 0.43-1.04 mm, 0.53-1.06 mm, and 0.77°-3.77° in the clinical studies and 0.23-1.04 mm, 0.24-2.13 mm, and 0.43°-3.78° in the in vitro studies, respectively. CONCLUSION: The accuracy of robotic CAIS in dental implant procedures appears to be within the clinically acceptable ranges. However, further relevant clinical trials validating the existing evidence are needed. CLINICAL SIGNIFICANCE: Robotic CAIS can achieve clinically acceptable implant placement accuracy. This innovative technology may improve the precision and success rates of dental implant procedures, with benefit for surgeons and patients.

[Effects and techniques of soft tissue augmentation in immediate implant treatment].

Immediate implant placement can reduce the number of treatments and the time without teeth, but it carries a higher aesthetic risk. Soft tissue augmentation can reduce the risk of gingival recession to a certain extent, improve the predictability and long-term stability of immediate implant aesthetics, and is currently a hot research topic. A comprehensive understanding of the evidence-based medicine and surgical techniques using soft tissue augmentation in immediate implant surgery can assist in clinical diagnosis, treatment decisions and improve treatment outcomes. This article elucidates the changes in soft and hard tissues after immediate implant placement, aesthetic risks, and risk factors. It also discusses the advantages, timing, material selection, and commonly used clinical techniques of soft tissue transplantation in immediate implantation, aiming to provide reference for clinical doctors to improve the effectiveness of immediate implantation.

Dilemma with implant placement in patients with florid cemento-osseous dysplasia: A literature review.

METHODOLOGY: An electronic search was done in PUBMED, SCOPUS, and a hand search was done in radiology, periodontology, and oral surgery journals. The search yielded 428 results, from which only 6 articles were selected for this literature review. Both prospective and retrospective studies were included. Clinical studies with information on the pre-implant condition of the site, detailed implant procedure, and follow-up after implant placement of more than 6 months were only considered for this review. RESULTS AND CONCLUSION: Limited clinical studies, shorter follow-up periods were the shortcomings of this review. However, it can be summarized that dental implants should not be placed at the site of FCOD, however can be placed at adjacent sites. Variations in implant type or the implant length had no bearing on the survival of implants at the sites of FCOD.

Navigating the future of guided dental implantology: A scoping review.

BACKGROUND: The aim of this scoping review was to understand the development of robotics and its accuracy in placing dental implants when compared to other forms of guided surgery. METHODS: An electronic search was conducted on the electronic databases of PubMed, Cochrane, and Science direct with the following queries: ((robotics) AND (dental implant)) AND (accuracy). The search timeline was between 2017 and 2022. RESULTS: A total of 54 articles were screened for title and abstract, of which 16 were deemed eligible for inclusion. Thirty-one articles were excluded mainly because they were out of topic (not relevant) or not in English. In total, 16 articles were included for analysis. CONCLUSIONS: This review thoroughly analyses 5 years of literature concerning the evolution of robotics in dental implant surgery, underscoring the necessity for additional research on nascent technologies reported and a comparative study with static and dynamic systems for clinical efficacy evaluation.

Free flap jaw reconstruction with dental implantation: A single-institution experience.

BACKGROUND: We sought to review our institution's experience with dental implant placement in free flap jaw reconstruction to determine factors impacting restoration of dental occlusion. METHODS: Exactly 48 patients underwent free flap jaw reconstruction with or without dental restoration from 2017 to 2022. Primary outcome was achievement of restored dental occlusion after jaw free flap reconstruction. RESULTS: A total of 48 patients with a mean age of 59.8 ± 16.4 years underwent jaw reconstruction from 2017 to 2022. Ten patients (20.8%) received osteointegrated dental implants. Two patients received a temporary dental prosthesis, 12 ± 4 months after initial reconstruction. Three patients received a final prosthesis, with a mean time to final prosthesis of 17.7 ± 12.4 months. Five patients did not receive any prosthesis despite placement of implants. CONCLUSION: A minority of patients received dental implant placement with free flap jaw reconstruction and only a small subset of these received a definitive dental prosthesis.